Artificial neural networks have utility in a wide variety of computing environments, such as speech recognition, process control, optical character recognition, handwriting recognition, continuous logic or fuzzy logic, engineering and scientific computations, signal processing, and image processing. Processing engines for many of the foregoing computing environments may be implemented through neural networks comprising a plurality of elemental logic elements called neuron circuits.
A neuron circuit (or processing element) is the fundamental building block of a neural network. A neuron circuit has multiple inputs and one output. As discussed in Related Invention No. 1 above, the structure of a conventional neuron circuit often includes a multiplier circuit, a summing circuit, a circuit for performing a non-linear function (such as a binary threshold or sigmoid function), and circuitry functioning as synapses or weighted input connections. Related Invention No. 1 discloses, in one embodiment, a neuron circuit which comprises only a multiplier as its main processing element.
While the neuron circuit disclosed in Related Invention No. 1 represents a very significant advance over the previously known prior art, it would be desirable to provide an improved neuron circuit which has at least the advantages of that disclosed in Related Invention No. 1 and which is even simpler and less expensive and which requires even less silicon space when implemented on an integrated circuit.
Therefore there is a significant need for a neuron circuit as described above and which can form the basis of a neural network which does not require lengthy training cycles and which converges on a global solution in a single training cycle.